def python_data_stream_example():
env = StreamExecutionEnvironment.get_execution_environment()
# Set the parallelism to be one to make sure that all data including fired timer and normal data
# are processed by the same worker and the collected result would be in order which is good for
# assertion.
env.set_parallelism(1)
env.set_stream_time_characteristic(TimeCharacteristic.EventTime)
type_info = Types.ROW_NAMED(['createTime', 'orderId', 'payAmount', 'payPlatform', 'provinceId'],
[Types.LONG(), Types.LONG(), Types.DOUBLE(), Types.INT(),
Types.INT()])
json_row_schema = JsonRowDeserializationSchema.builder().type_info(type_info).build()
kafka_props = {'bootstrap.servers': 'localhost:9092', 'group.id': 'pyflink-e2e-source'}
kafka_consumer = FlinkKafkaConsumer("timer-stream-source", json_row_schema, kafka_props)
kafka_producer = FlinkKafkaProducer("timer-stream-sink", SimpleStringSchema(), kafka_props)
watermark_strategy = WatermarkStrategy.for_bounded_out_of_orderness(Duration.of_seconds(5))\
.with_timestamp_assigner(KafkaRowTimestampAssigner())
kafka_consumer.set_start_from_earliest()
ds = env.add_source(kafka_consumer).assign_timestamps_and_watermarks(watermark_strategy)
ds.key_by(MyKeySelector(), key_type_info=Types.LONG()) \
.process(MyProcessFunction(), output_type=Types.STRING()) \
.add_sink(kafka_producer)
env.execute_async("test data stream timer")
def test_for_bounded_out_of_orderness(self):
jvm = get_gateway().jvm
j_watermark_strategy = WatermarkStrategy.for_bounded_out_of_orderness(
Duration.of_seconds(3))._j_watermark_strategy
j_watermark_generator = j_watermark_strategy.createWatermarkGenerator(
None)
self.assertTrue(
is_instance_of(
j_watermark_generator, jvm.org.apache.flink.api.common.
eventtime.BoundedOutOfOrdernessWatermarks))
self.assertEqual(
get_field_value(j_watermark_generator, "outOfOrdernessMillis"),
3000)
